About the Business

At Moeve, we're driving a transformative strategy called "Positive Motion," focused on greening our revenue streams. As part of this vision, our Trading team plays a crucial role in optimizing value from both legacy hydrocarbons and emerging energy flows. Trading is key to maximizing value by expanding and leveraging our platform. The Risk team plays a key role in supporting trading activities by developing and maintaining cutting-edge tools and models to measure and manage risk effectively across the portfolio.

As part of the Modelling & Analytics team, you will contribute to the development of Moeve's counterparty credit risk capabilities. Your primary focus will be on building robust, transparent and scalable methodologies to measure and aggregate credit exposure at trade, counterparty and portfolio level, incorporating contractual risk mitigants and concentration risk. You will also support pragmatic Probability of Default modelling for Expected Loss and Unexpected Loss measurement.

The Responsibilities We'll Trust You With

• Develop and maintain methodologies and tools to measure and aggregate counterparty credit exposure at trade, counterparty and portfolio level across energy commodities, derivatives and physical contracts.
• Develop quantitative models to simulate the evolution of commodity market risk factors and generate future exposure profiles and metrics, including Current Exposure, Expected Exposure (EE) and Potential Future Exposure (PFE), across different pricing, delivery and settlement windows.
• Model the impact of contractual credit risk mitigants on exposure, including netting, collateral, margining, guarantees, payment terms and other relevant contractual provisions.
• Analyse portfolio credit risk and concentration across counterparties, sectors, geographies, commodities and maturity profiles, considering aggregation, diversification, common risk factors and stress scenarios.
• Develop pragmatic methodologies for Probability of Default (PD) and Loss Given Default (LGD) estimation, combining available quantitative indicators, external information and structured qualitative risk factors.
• Integrate exposure profiles, PD and LGD assumptions into Expected Loss (EL) and Unexpected Loss (UL) metrics to support counterparty limits, risk appetite and portfolio risk analysis.
• Automate and enhance recurring credit risk calculations, controls and reporting, ensuring accuracy, scalability, traceability and timely delivery.
• Translate credit risk requirements, contractual terms and trading structures into robust quantitative methodologies, providing exposure and portfolio risk analytics to support credit monitoring, limit setting and risk decision-making.
• Document methodologies, data sources, assumptions, controls and limitations, and support model validation, governance and audit requirements.

What We Are Looking For

• 2–3 years of experience in quantitative risk, counterparty credit risk, market risk, financial modelling or commodity trading analytics. Candidates with slightly less experience may also be considered where they demonstrate strong quantitative modelling and programming capabilities.
• Degree in Mathematics, Physics, Engineering, Statistics, Computer Science, Quantitative Economics or another relevant quantitative discipline.
• Solid programming skills in Python, including experience with numerical and data analysis libraries such as NumPy, Pandas and SciPy. Knowledge of SQL and experience working with structured data are highly desirable.
• Solid understanding of probability, statistics, stochastic modelling and simulation techniques, including Monte Carlo methods.
• Understanding of financial risk measurement and familiarity with counterparty credit exposure, netting, collateral or other contractual risk mitigants.
• Good English and Spanish communication skills, both verbal and written.

Competencies

• Strong analytical mindset, with the ability to break down complex exposures, challenge modelling assumptions and identify the key drivers of risk.
• High attention to detail, data quality, controls and the reproducibility of quantitative results.
• Practical problem-solver, comfortable investigating data issues, methodological limitations and unexpected model outputs.
• Clear communicator and collaborative team player, able to explain quantitative concepts and work constructively across risk, business and technology functions while maintaining ownership of assigned deliverables.
• Curious and committed to continuous learning, particularly in counterparty exposure, energy markets, credit risk mitigation and portfolio risk.
• Organized and reliable, with the ability to manage competing priorities and deliver robust analytical work in a dynamic environment.

Moeve is committed to ensuring equal opportunities, identifying and developing the full potential of individuals based solely on their abilities to perform their roles and without discrimination based on sexual orientation, gender identity, gender expression, sexual characteristics, or any other aspect of diversity.